Variational autoencodercông việc
...demo — from development to live delivery. Scope of Work Live Technical Demonstration (Immediate) Develop, test, and present live the following 8 quantum algorithms at 40-qubit scale: Quantum Fourier Transform (QFT) Quantum Approximate Optimization Algorithm (QAOA) Shor’s Algorithm (full statevector simulation, simplified for live execution) Grover’s Algorithm (full statevector simulation) Variational Quantum Eigensolver (VQE) Quantum Noise Simulation (multiple noise models) Quantum Machine Learning (QCNN & HLL) Execute algorithms live on a high-performance cloud GPU backend (NVIDIA A100 + cuQuantum or equivalent). Ensure stable, uninterrupted performance during the live presentation. Prepare backup strategies for connectivity, API delays, or simulation ...
...integration for production deployment. The exact platform for the demo (cloud vs. on-prem) is being finalized — flexibility is required. Scope of Work: Phase 1: Technical Demonstration (Immediate) Develop and execute 7 quantum algorithms at 40-qubit scale: Quantum Fourier Transform (QFT) Quantum Approximate Optimization Algorithm (QAOA) Shor’s Algorithm (simplified, scalable) Grover’s Algorithm Variational Quantum Eigensolver (VQE) Quantum Noise Simulation Quantum Machine Learning (QCNN/HLL) Deliverables: Clean, documented code (Python/Jupyter) Screenshots/logs of successful execution Performance metrics (runtime, memory usage) Brief technical report explaining methods and results Phase 2: System Integration Advisory (Optional follow-on) Support deployment...
...Control OpenFlow-enabled SDN controller Global view of VANET traffic behavior Intelligent DDoS Detection Flow-level traffic analysis Anomaly detection using ML / Deep Learning (LSTM Autoencoder / Hybrid model) Real-Time Mitigation Strategy Malicious vehicle flow isolation Dynamic rule installation at RSUs & switches Rate limiting and blacklisting of attack sources VANET-Specific Security Awareness Handles high mobility and dynamic topology Maintains low latency for safety messages Technologies Used SDN Controller: Ryu / ONOS VANET Simulator: SUMO + Mininet-WiFi ML/DL Model: LSTM Autoencoder / SVM / Random Forest/ use other technique than LSTM can use multimodel for transfer learning Dataset: SDN Specifc dataset Protocols: OpenFlow, TCP/UDP Programm...
...learn old operating regimes *Features lose meaning due to control changes *Label scarcity blocks retraining Include one mini-example from a paper: *“Model trained on Month A fails on Month B” type result *You cite it and explain why it happened 4. Methods and drift-handling solutions (1.5–2 pages) Compare only 4 method families to keep scope doable: Pick 2 detector families: *Unsupervised (Autoencoder, Isolation Forest, PCA) *Time-series deep models (LSTM/GRU/Transformer) *Graph-based (GNN) if you want topology awareness Pick 2 drift-handling strategies: *Drift detectors (ADWIN, Page-Hinkley, DDM) *Sliding window + periodic retraining *Online learning (River-style) *Ensembles that replace stale models *Physics-informed constraints (power laws, limits) Delivera...
...ready for you to turn into a robust signal-generating engine. The dataset must first be converted to Renko bricks; from there, I want both supervised and unsupervised deep-learning approaches explored so we can isolate the technique that flags high-probability entry and exit points most reliably. The core need is model generation. You’ll select or design the architecture—LSTM, Transformer, autoencoder hybrids, or any framework that proves effective—then train, validate, and benchmark it. I can supply continuous data feeds for additional walk-forward testing once an initial model is in place. You’ll run the experiments on your own GPU or on AWS you provision; if a different cloud or on-prem solution will accelerate training, outline that and we’ll s...
...with nothing skipped . 3) MATLAB Illustrative Example(s) MATLAB 2025 has many AI tools — prefer built-in functionality only for visualization and diagnostics; the core algorithm must be derived and coded manually for educational clarity. Topics to Cover 1. Regression 2. Classification 3. kNN & Decision Tree 4. Clustering: K-means 5. Dimension Reduction 6. Artificial Neural Networks (ANN) 7. Autoencoder 8. Convolutional Neural Networks (CNN) 9. Explainable AI (XAI) 10. Recurrent Neural Networks (RNN) 11. Transfer Learning 12. Physics Informed Neural Network 13. Generative AI diffusion models, transformers Deliverables For each topic: - Microsoft PowerPoint presentations using standard slide layout (size is wide screen 16:9), readable fonts, and color contrast suit...
...Learning, and Software Defined Networking (SDN) to develop a complete project titled: “Intelligent Detection Mechanism for DDoS Attack Prevention in SDN-VANET.” The goal is to design and implement an intelligent, ML/DL-based detection and mitigation system that can detect Distributed Denial of Service (DDoS) attacks in SDN-enabled Vehicular Ad-hoc Networks (VANETs). 1. Detection Module: Use LSTM Autoencoder (unsupervised) or any efficient deep learning algorithm for DDoS anomaly detection. Optionally integrate Federated Learning (FedAvg) across RSUs/OBUs to simulate distributed learning without sharing raw data. Use datasets like CIC-DDoS2019, CIC-IDS2017, or CAIDA DDoS dataset for training and evaluation. Implement feature extraction (e.g., via CICFlowMeter or ...
...to learn normal behaviour on its own, then flag anything unusual in real time. Preferred approach My goal is to train an autoencoder and an Isolation Forest on historical data so the model understands the “normal envelope” of the environment. Once trained, the lightweight inference layer should run locally on the ESP32 if memory allows; otherwise a small client on the board may stream data to a cloud endpoint where the model executes. In either case the detection latency must stay low enough for prompt alerts. Scope of work • Prepare or augment the dataset, handle scaling and any time-windowing that benefits the model. • Build, train, and evaluate both the autoencoder and the Isolation Forest, selecting whichever meets accuracy and footprint require...
Proposed Paper Title: “AI-Driven Fraud Detection in Financial Transactions Using Hybrid Machin...financial stability, data integrity, and cyber risk mitigation. Key Methodology Components: Data Collection & Preprocessing – Simulate or use anonymized bank transaction data (Kaggle, IEEE DataPort, or proprietary datasets). Feature Engineering – Identify behavioral and transactional anomalies. Modeling Techniques – Combine: Random Forest or XGBoost (for structured fraud classification) Autoencoder or LSTM (for time-series anomaly detection) Generative AI (for synthetic fraud pattern generation) System Architecture – Implement a real-time fraud detection pipeline on cloud (AWS or Azure). Evaluation Metrics – Precision, Recall, F1-score, a...
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...learn normal behaviour on its own, then flag anything unusual in real time. Preferred approach My goal is to train an autoencoder and an Isolation Forest on historical data so the model understands the “normal envelope” of the environment. Once trained, the lightweight inference layer should run locally on the ESP32 if memory allows; otherwise a small client on the board may stream data to a cloud endpoint where the model executes. In either case the detection latency must stay low enough for prompt alerts. Scope of work • Prepare or augment the dataset, handle scaling and any time-windowing that benefits the model. • Build, train, and evaluate both the autoencoder and the Isolation Forest, selecting whichever meets accuracy and footprint requ...
The task is to build a concise yet fully functional variational autoencoder that learns meaningful latent representations from X-ray images and accurately reconstructs them. I will provide an anonymised chest X-ray subset, or you may suggest a publicly available alternative that is easy to obtain and free of personal identifiers. Python 3 with PyTorch or TensorFlow/Keras is preferred. Keep the code modular, well-commented and GPU-ready so it can double as teaching material for my master’s project. The training routine should accept configurable hyper-parameters from a single file, log losses per epoch and save the best model checkpoints automatically. Deliverables • Clean source code with or • Jupyter notebook demonstrating data loading, training, re...
I need an AutoEncoder specifically designed for compressing Computational Fluid Dynamics (CFD) images and making predictions based on them. Key Requirements: - Primary application: Image compression and prediction - Type of data: CFD images - Output format: To be determined (preferably JPEG, PNG, or TIFF) Ideal Skills and Experience: - Proficiency in deep learning and neural networks - Experience with AutoEncoders, especially for image processing - Knowledge of CFD data and its intricacies - Familiarity with image output formats and conversion Please share your relevant experience and proposed approach.
...introduce multi-agent reinforcement learners that propose novel scaffolds, rank candidates and feed discoveries back to the central datastore. Deliverables for the initial phase 1. Reproducible environment (conda or Docker) with all dependencies pinned. 2. Data ingestion scripts plus a populated PostgreSQL schema using a provided sample set. 3. A working hybrid model: classical GNN + quantum variational circuit, trained on an activity dataset and benchmarked against a pure classical baseline. 4. Documentation and a short Jupyter notebook walkthrough that lets a scientist reproduce results on a GPU-equipped workstation. Acceptance criteria • End-to-end pipeline runs with one command and completes without manual intervention. • Hybrid model achieves statisti...
...Interpretation**: 1–3 bullets explaining results. MATLAB 2025 has many AI tools — prefer built-in functionality **only for visualization and diagnostics**; the **core algorithm must be derived and coded manually** for educational clarity. ## Topics to Cover 1. Regression 2. Classification 3. kNN & Decision Tree 4. Clustering: K-means 5. Dimension Reduction 6. Artificial Neural Networks (ANN) 7. Autoencoder 8. Convolutional Neural Networks (CNN) 9. Explainable AI (XAI) 10. Recurrent Neural Networks (RNN) 11. Transfer Learning --- ## Deliverables For **each topic**: - A **step-by-step mathematical explanation** (clear, no skipped steps). - A **numerical method/algorithm section** (flow + pseudocode). - A **MATLAB example** (synthetic dataset, training, visual...
...scalability (batch processing, streaming optional). Document logic and recommend improvements to enrich pattern recognition over time. Ideal Skillset: Strong Python skills (especially in data wrangling and NLP). Experience with MongoDB (PyMongo, aggregation pipelines). Familiarity with Hugging Face Transformers (T5, FLAN-T5, Mistral, etc.). Experience building or fine-tuning LSTM, Transformer, or Autoencoder models. Regex generation/mining tools (e.g., refex, AutoRegex, regexgen). Clustering algorithms and similarity measures (Levenshtein, n-gram, FAISS). Familiarity with web scraping or URL structure analysis is a plus. Comfortable working with large datasets (millions of rows). Tools & Frameworks You Might Use: Python (Pandas, Numpy, Scikit-learn, PyTorch/TensorFl...
...integrated with variational and state-based components. We're seeking a deep learning expert to help stabilize the model, improve reward modeling, and mitigate mode collapse in our current implementation. Your Responsibilities Diagnose and fix mode collapse and repetitive output issues during training Refine or replace current reward-weighted supervision and false confidence injection strategies Improve model stability, output diversity, and training convergence Propose and implement architectural improvements to the custom VAE-HMM-Transformer backbone and other components of the Cerebrum Optionally: help design better rule-based or learned reward functions Required Skills Strong experience with PyTorch and transformer architectures Deep understanding of variation...
...integrated with variational and state-based components. We're seeking a deep learning expert to help stabilize the model, improve reward modeling, and mitigate mode collapse in our current implementation. Your Responsibilities Diagnose and fix mode collapse and repetitive output issues during training Refine or replace current reward-weighted supervision and false confidence injection strategies Improve model stability, output diversity, and training convergence Propose and implement architectural improvements to the custom VAE-HMM-Transformer backbone and other components of the Cerebrum Optionally: help design better rule-based or learned reward functions Required Skills Strong experience with PyTorch and transformer architectures Deep understanding of variation...
...COMPREHENSIVE TECHNICAL SPECIFICATIONS: Quantum Algorithm Development: Design novel quantum algorithms for: Portfolio optimization (beyond basic QAOA approaches) Monte Carlo simulation acceleration (quantum amplitude estimation) Credit risk analysis using quantum machine learning Derivative pricing with quantum differential equations Market crash prediction using quantum neural networks Implement variational quantum algorithms (VQE, QAOA, QNN) with custom ansatz Develop quantum error mitigation strategies for NISQ devices: Zero-noise extrapolation Probabilistic error cancellation Symmetry verification protocols Custom error models for financial calculations Create quantum circuit optimization techniques: Gate reduction algorithms Qubit routing for specific quantum hardwar...
Research paper on Theoretical Comparison of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for Enhanced Renewable Energy Management
I need an expert to develop a privacy-preserving and anomaly detection for Intrusion Detection System (IDS) for IoT networks. The system should leverage: - Semi-supervised learning - Unsupervised feature extraction - Federated learning Key functionalities include: - Anomaly detection using an autoencoder - K-Means clustering - Distributed training across edge devices Ideal skills and experience: - Strong background in machine learning and cybersecurity - Experience with federated learning and anomaly detection - Familiarity with IoT networks and privacy-preserving technologies Please provide relevant work experience.
... --- 3. Market Access FIX API Live Market Data Stream: port 5211 Trade Execution Stream: port 5212 Assets Forex Majors & Crosses Indices: US30, NAS100, SPX500, DAX40 Commodities: Gold, Silver, Oil Crypto: BTC, ETH, EOS --- 4. Core Functions (Grouped) 4.1 AI Strategy Engine (Python) Multiple AI models working in parallel: Trend Following (Transformer, LSTM) Mean Reversion (Autoencoder + XGBoost) Scalping (Reinforcement Learning with PPO/DQN) Breakout Detection (CNN + LSTM) Ensemble Learning selects best strategy based on market phase 4.2 Adaptive Trade Execution (C++) Smart Order Selection: Market, Limit, Iceberg, Dark Pool routing Order modification & cancellation Real-time latency & slippage measurement Auto-adaptive Grid + Martingale sys...
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I need a Machine Learning expert to refine and extend an existing dimensionality reduction analysis on a financial dataset. The current implementation includes PCA & Variational Autoencoder (VAE), but requires further modifications: ✅ Implement a third Autoencoder (e.g., Convolutional Autoencoder) ✅ Apply PCA & Autoencoders on both training and test sets and analyze generalization ✅ Compare reconstruction errors between training & test data ✅ Analyze key components/features for all four methods (PCA, VAE, Autoencoders) ✅ Fix minor visualization issues (integer axis labels, English-only text, formatting) Requirements: ? Strong expertise in Python (TensorFlow, PyTorch, NumPy, Pandas, Scikit-learn) ? Experience with Autoencoders & Dimensionality R...
I'm seeking an expert in reinforcement learning and machine learning with a focus on digital communication systems. The project involves utilizing RL-based autoencoders to enhance an end-to-end wireless communication system, specifically dealing with noisy feedback scenarios. Key Tasks: - Design an RL-based autoencoder system aimed at optimizing channel use and enhancing overall communication efficiency. - Apply machine learning techniques to improve aspects of digital communication such as error correction, signal processing, and channel estimation. - Ensure the system is resilient to noise and can effectively handle digital communication challenges. Ideal Skills: - Proficiency in reinforcement learning and machine learning. - Extensive knowledge of digital communication sys...
I'm looking for a professional to create a detailed and high-quality visualization of my deep learning model, which is a CNN + Transformer Autoencoder. The diagram should clearly illustrate the encoder, decoder, latent space, feature maps, and their connections. Key requirements for the project: - The final architecture diagram should be SVG or vector graphics. - The diagram must use contrasting colors and be structured in a clear and easy to understand manner. - The diagram needs to include layer names, feature map dimensions, and additional comments. Specifics of the model: - The encoder consists of Conv1D, Dense, and Transformer (Multi-Head Attention). - The latent space is used for feature extraction. - The decoder includes Conv1DTranspose and Dense. - The input to the mo...
...Profile: Age Weight Lifestyle & Dietary Preferences Health Markers: NAD+ Levels Cortisol Levels Sleep Patterns Nutrient-Frequency Mapping: Data Sources: USDA FoodData Central Spectral Absorption Databases Molecular Vibration Data Mapping Mechanism: Fourier Transform of Nutrient Vibrational Frequencies Subharmonic and Harmonic Frequency Computation 2.2 AI Processing Layer Data Preprocessing: Variational Autoencoder (VAE) encodes user vitality states. RNN-based models predict optimal RFM sequences based on past biofeedback. Biofield Resonance Analysis: Quantum magnetometry assesses biofield entropy fluctuations. Predictive AI models real-time energy alignment using AI-driven resonance algorithms. RFM Generation Model: Generative AI synthesizes soundscapes based on 432 Hz...
...compression, high quality, and low maintenance costs: 1. Data Preparation: • Frame Extraction: Decompose the video into individual frames. • Block Segmentation: Divide each frame into non-overlapping blocks (e.g., 8x8 pixels) to create a set of image patches. 2. Feature Extraction with Deep Learning: • Autoencoder Training: • Architecture Design: Develop an autoencoder neural network tailored for image data, comprising an encoder and a decoder. • Training: Train the autoencoder on the image patches to learn a compact representation (latent space) of the input data. • Latent Vector Generation: Utilize the encoder to transform each image patch into its corresponding latent vector, capturing essential features in a reduced-dimens...
...formatting standards and includes placeholders for each section 3. Implementation and Validation of Cryptographic Mechanisms Objective: Recreate or develop code to implement the hybrid cryptographic mechanism (ALO-DHT) described in the original article. Steps: Reproduce the Ant Lion Optimization algorithm and its integration with the Diffie-Hellman and Twofish cryptographic techniques. Integrate Autoencoder neural networks for anomaly detection as described in the article. Expected Output: Well-documented code with explanations for each component, ideally in Python. 4. Conducting Tests and Gathering Results Objective: Run simulations or tests to gather results comparable to those in the original article, specifically metrics like accuracy, precision, recall, time consumption, and...
...implementation is mostly complete, but there are a few bugs that need to be resolved.] I am looking for someone to assist with a project focused on generative models, specifically involving Variational Auto-Encoders (VAE) and Auto-Decoders. The task includes training an Auto Decoder on the Fashion MNIST dataset, minimizing the reconstruction loss, and analyzing the latent space. You will need to justify model architecture and training parameter choices, evaluate the model on both training and test sets, and generate visual results by decoding latent vectors. Skills required: - Deep understanding of Auto-Encoders and Variational Auto-Encoders (VAE) - Experience with generative models and latent space exploration - Proficiency in Python and deep learning libraries (e.g., P...
I need assistance with a nonlinear integral equation. I'm particularly interested in using analytical methods to solve it, with a focus on variational methods as the preferred approximation technique. Ideal skills and experience for the job: - Strong background in mathematical analysis - Proficiency in solving nonlinear integral equations - Experience in using variational methods - Ability to explain complex concepts in a simple manner - Good communication skills to discuss progress and challenges
I'm looking for an expert in quantitative analysis to help me solve specific nonlinear parabolic equations. The project involves: - Deriving universal estimates and Liouville theorems for superlinear problems - Conducting a thorough theoretical analysis - Utilizing variational methods, scaling method and doubling lemma. Ideal candidates should have a strong background in qualitative and quantitative analysis, and experience with perturbation techniques, and variational methods.
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...seamless deployment and address any integration issues. 6. Ongoing Support and Maintenance: Provide ongoing support for any technical issues. Regularly update and improve the AI models and APIs. Required Skills and Knowledge Technical Skills: Deep Learning Frameworks: Proficiency in TensorFlow, PyTorch, or similar frameworks. Generative AI: Experience with Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models. Voice Cloning: Knowledge of voice synthesis techniques and tools (e.g., WaveNet, Tacotron). Image Processing: Expertise in image transformation and enhancement algorithms. Video Synthesis: Skills in generating high-quality videos from images. API Development: Experience in developing and documenting RESTful APIs. Professional...
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I am in need of an image compression model implemented in Python, or by Matlab for medical datasets. The primary aim is to achieve high compression levels on MRI and CT scans while maintaining diagnostic quality. Key Requirements: - High Compression Rate: We are prioritizing a small file size to allow for efficient storage and transfer of image data. - Image Quality: The final compressed images should retain a high level of visual quality, critical for accurate medical diagnosis. - Dataset Expertise: This project is focused on MRI scans and CT scans. Any experience with handling similar medical imaging datasets is a plus. Ideal Skills and Experience: - Strong programming skills in Python or Matlab. - Profound understanding of image processing. - Experience with autoencoders, particular...
...code base and dataset I provide, construct an Abstract Syntax Tree (AST) based on them, and then create an Autoencoder using TensorFlow to compress the resulting AST for a classification problem. Key requirements for this project include: - Proficiency in Python: You will be working with a Python code base and dataset. Familiarity with Python is essential for this project. - Understanding of AST: You should be able to construct an AST tree based on the provided dataset and code base. - TensorFlow Experience: I would like the Autoencoder to be developed using TensorFlow, so prior experience with this framework is a must. - Output format: The desired output format for the Autoencoder is JSON, so you should be comfortable working with this format. This project is i...
...knowledge in the use of autoencoders for feature extraction and GRU models. Key project elements include: * Professionals will need to implement an autoencoder for feature extraction with the core goal of reducing data dimensionality. Prior experience in working with high-dimensional data and in deploying autoencoders is necessary. * Subsequently, the system built should be capable of classifying Denial of Service (DoS), User to Root (U2R), and Probe attacks within the KDD dataset. Good working knowledge of GRU models and the mentioned attacks is required. * The project includes steps to ensure the accuracy and reliability of both the autoencoder and GRU model. This includes: 1. Hyperparameter Tuning: Applicant should be dexterous in practicing various optimization te...
The main task is to create the variational autoencoder.
For this project, I'm seeking a skilled machine learning engineer with proficiency using TensorFlow jupyter notebook to create a variational autoencoder model. Your task will be to detect anomalies related to mobility patterns within an Excel-format dataset. Key Tasks Include: - Analyzing a large dataset with more then am million rows and 32 columns. - Building a variational autoencoder in TensorFlow specifically designed to identify anomalies in mobility patterns. -Visualize the result between two time Ideal skills and experience: - Proficient in TensorFlow and machine learning algorithms. - Experience with variational autoencoder . - Demonstrable expertise in anomaly detection algorithms, particularly in mobility patterns data.
I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.
I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.
I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.
I'm seeking an adept freelancer with experience in generative AI to create synthetic data. This project aims to utilise synthetic data to effectively train machine learning models. A solid command in various generative AI techniques, specifically Variational Autoencoders, Generative Adversarial Networks, and PixelRNN, is required. Key Responsibilities: • Generate synthetic data of 1 Million questions and answers using GenAI techniques • Ensure the generated data is adequate for training generative AI • Create a robust framework for producing questions and answers pairs Ideal Candidate: •used to with Chat GPT • Background in synthetic data generation
This example shows how to train a deep learning variational autoencoder (VAE) to generate images. Ideal Skills and Experience: - Proficiency in MATLAB and experience with training Variational Autoencoders. - Strong understanding of image processing and deep learning techniques. - Ability to work with different input and output image sizes. - Familiarity with generating images using VAEs. If you are confident in your MATLAB skills and have experience with VAEs, please submit your proposal for this project.
I require a deep learning model where the nodes of a hop field network are made up of variational auto encoders. Where the number of node is expandable
...skilled Python developer to modify an existing autoencoder code. The code is written in Python and requires changes in the input of the autoencoder. Skills and Experience: - Proficiency in Python programming language - Strong understanding of autoencoders and their implementation - Experience in modifying existing code and making necessary changes - Familiarity with data preprocessing and manipulation techniques in Python Tasks: - Modify the input of the autoencoder code to meet the project requirements - Ensure the code runs efficiently and effectively - Debug and fix any issues that may arise during the modification process - Implement any additional features or improvements as needed Deliverables: - Updated Python code with the modified autoencoder inpu...
...changing market conditions Hybrid algorithm: RL algorithm: Deep Q-networks (DQNs) DL algorithm: Autoencoder EA: Neuroevolution Explanation: The RL algorithm would be used to learn a trading strategy that can adapt to changing market conditions. The autoencoder would be used to learn a compressed representation of market data. The neuroevolution algorithm would be used to evolve the RL algorithm to adapt to changing market conditions. The RL algorithm would be trained on a dataset of historical market data. The RL algorithm would learn to make trading decisions that are based on the current market conditions. The autoencoder would be trained on the same dataset of historical market data. The autoencoder would learn a compressed representation of market dat...
...Deep Learning, Computer Vision, Natural Language Processing, Algo Trading. Expertise: Training Machine learning, Deep Learning, Reinforcement Learning Models Frameworks are compatible with Pytorch, Tensorflow, Keras. Training ML models like Bayesian Networks, XGBoost, Random Forest, Decision Tree, KNN Classifier, GBM, GLM, SVM. Training DL models BERT and all its variants, DNN, CNN, LSTM, GRU, Variational Autoencoders using Bayesian Neural Networks. eXplainable AI (XAI) using LIME, SHAP, GRAD-CAM, DeepLIFT, DeepRED, CausalNex. Here are some of our previous works: Hair transplant startups require some image generation for their customers. Masters Thesis in Art Generation using GANs Head Gesture recognition using temporal signal data Hairstyle Classification Heartbeat recognition u...
